From the Lineberger Comprehensive Cancer Center and Departments of Genetics, Pathology and Laboratory Medicine, and Department of Statistics and Operations Research, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC; Department of Pathology, University of Utah Health Sciences Center; ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT; Genetic Pathology Evaluation Centre, Department of Pathology, Vancouver Coastal Health Research Institute; Departments of Pathology and Radiation Oncology, British Columbia Cancer Agency; Department of Pathology, University of British Columbia, Vancouver, British Columbia, Canada; Genome Sequencing Facility and Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO; and Department of Pathology, Thomas Jefferson University, Philadelphia, PA.
J Clin Oncol. 2023 Sep 10;41(26):4192-4199. doi: 10.1200/JCO.22.02511.
To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression-based "intrinsic" subtypes luminal A, luminal B, HER2-enriched, and basal-like.
A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen.
The intrinsic subtypes as discrete entities showed prognostic significance ( = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%.
Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.
通过开发一种纳入基于基因表达的“固有”亚型 luminal A、luminal B、HER2 富集和基底样的风险模型，改进当前乳腺癌预后和化疗获益预测的标准。
使用来自 189 个原型样本的微阵列和定量逆转录聚合酶链反应数据开发了 50 个基因亚型预测器。对来自 761 名（无系统治疗）患者的测试集进行了预后评估，对 133 名患者进行了预测对紫杉烷和蒽环类药物方案的病理完全缓解 (pCR) 的评估。
作为离散实体的固有亚型显示出预后意义 (= 2.26E-12)，并且在纳入标准参数（雌激素受体状态、组织学分级、肿瘤大小和淋巴结状态）的多变量分析中仍然具有显著性。使用固有亚型和临床信息为淋巴结阴性乳腺癌建立了预后模型。联合模型（亚型和肿瘤大小）的 C 指数估计值显著优于临床病理模型或单独的亚型模型。固有亚型模型预测新辅助化疗的疗效，pCR 的阴性预测值为 97%。
固有亚型的诊断为乳腺癌患者的标准参数增加了重要的预后和预测信息。连续风险评分的预后特性对于管理淋巴结阴性乳腺癌将具有重要价值。亚型和风险评分还可用于评估新辅助化疗的疗效可能性。
